Status and Outlook of
the OMI Surface UV
(OMUVB) product
OMI Science Team Meeting
Baltimore, June 7, 2007
Aapo Tanskanen
OMI Surface UV Algorithm
E  Ecs (, , Rs , z )  CMF ( c , , Rs , z)  AC ( a , , Rs ,  , c )
Ecs clear-sky irradiance
CMF cloud modification factor
AC aerosol correction
z altitude
 total column ozone
Rs surface albedo
 solar zenith angle
 c cloud optical depth
 a aerosol optical depth
 single scattering albedo
• OMTO3 Level 2 data contains the
satellite measurement data
required for calculation of the first
two terms
• However, the diurnal variation of
the cloud conditions is not caught
by using only OMI measurements
• Methods and sources of data for
aerosol correction are being
investigated. The intention is to
introduce an aerosol correction in
ECS 3.
Example: UV Index (clear-sky and cloud corrected)
Processing Status and Data Release
• The currently processed ECS 2 based OMUVB data
corresponds to a time period from the launch of Aura to
July 27, 2006 (last orbit 10744)
• AVDC provides OMUVB overpass data for over 100 sites
that has been used for validation. New sites can be added
(Bojan.Bojkov@gsfc.nasa.gov)
• Level 2 HDF5-EOS and Level 3 (1x1 degrees TOMS) data
are available at FMI's FTP site, and will become available
at DAAC this summer.
• http://omi.fmi.fi/OMUVB_readme.html
• FMI has developed a web application for online plotting of
OMUVB data with GrADS using 1x1 degrees gridded data
Summary of the findings of the validation paper
submitted to the JGR special issue on Aura validation:
VALIDATION OF DAILY ERYTHEMAL DOSES FROM OMI WITH GROUND-BASED UV MEASUREMENT DATA
Aapo Tanskanen(1), Anders Lindfors(1), Anu Määttä(1), Nickolay Krotkov(2), Jay Herman(3), Jussi Kaurola(1), Tapani Koskela(1),
Kaisa Lakkala(4), Vitali Fioletov (5), Germar Bernhard (6), Richard McKenzie(7), Yutaka Kondo(8), Michael O'Neill(9),
Harry Slaper(10), Peter den Outer(10), Alkiviadis F. Bais(11), Johanna Tamminen(1)
(1) Finnish Meteorological Institute, Helsinki, Finland
(2) GEST Center, University of Maryland, Baltimore, USA
(3) NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
(4) FMI’s Arctic Research Centre, Sodankylä, Finland
(5) MSC/Environment Canada, Ontario, Canada
(6) Biospherical Instruments, San Diego, USA
(7) National Institute of Water and Atmospheric Research, Lauder, Central Otago, New Zealand
(8) University of Tokyo, Tokyo, Japan
(9) Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, USA
(10) National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
(11) Aristotle University of Thessaloniki, Laboratory of Atmospheric Physics, Thessaloniki, Greece
• Several additional groups are validating the OMI surface UV data, which
indicates that there is a great interest in this product
OMUVB Validation
• Daily erythemal doses derived from OMI
measurements were compared with those calculated
from ground-based measurements
• Science questions
• Can we continue the TOMS UV time series with surface UV
derived from OMI measurements?
• Is the plane-parallel-cloud (PPC) model based method for cloud
correction superior to the simple Lambertian Equivalent
Reflectivity (LER) based cloud correction method?
• Does the new surface albedo climatology fix the problem of the
underestimation of surface UV at seasonally snow covered
terrain?
Ground-based Reference Data
•17 measurement sites
representing various
measurement conditions
•18 spectral UV instruments
with high level QA/QC
Analysis of the comparison results
• Scatterplots, error distributions
• snow cover (Rs>0.10)
• snow free (Rs<0.10)
• Statistical quantities
• Median bias (less sensitive to outliers
than the average)
• Percentages of the OMI-derived doses
within 10, 20, and 30% with the
reference data
• Usual quantities, such as correlation
coefficient and root-mean-square were
abandoned, because correlation
originates mostly from seasonality and
the error distributions are not normal
distributions
•
OMI surface UV does not catch the diurnal variation in cloud conditions,
because attenuation of the UV radiation by clouds is estimated using a single
overpass measurement
•
The clouds over the Greenland icecap are optically thin, and therefore, their
effect on surface UV is small. For a typical reference site, the satellite-derived
daily doses differ more from the reference data because of uncertainty related
to cloud attenuation.
• Relative uncertainty of the OMI derived dose increases as a
function of the observed cloud optical thickness
• Negative bias at Mauna Loa, due to
• Scattering from air and from highly reflecting
clouds below the observation site increase the
effective albedo and the observed UV doses
• The satellite-derived ozone column represents
an average over a large footprint, whereas the
ozone column above the elevated observatory
is systematically about 5% less than the mean
• Small positive bias at clean sites
• Positive bias at sites affected by absorbing aerosoles
or trace gases
rural
urban
Arctic
forest
fires?
urban
megacity
haze?
city
• Surface albedo climatology works at some polar sites, but fails at
some other. Coastal Antarctic sites are extremely challenging for
the surface UV algorithm
NSF monitoring site at Palmer
100
Summit
PPC summer
PPC winter
LER summer
LER winter
80
Eureka
Satisfactory
60
W20 (%)
Mauna Loa
40
Positive bias due
to tropospheric extinction
Negative bias
because of
underestimated
surface albedo
20
0
0
0.2
0.4
0.6
0.8
1
Median r
1.2
1.4
1.6
1.8
2
Conclusions
• OMI measurements are suitable for continuation of the
global satellite-derived surface UV time series using a
surface UV algorithm similar to the original TOMS UV
algorithm
• Two alternative cloud correction methods were compared:
plane-parallel cloud model method and the method based
on Lambertian equivalent reflectivity
• One cloud correction method was not found systematically
superior to the other
• However, a comparison of spectral irradiances would likely
show the advantages of the plane-parallel cloud method that
accounts for the spectral dependency of cloud modification
factor
• Validation of the spectral irradiances are needed in order
to better quantify the positive bias of the satellite-derived
UV due to absorbing aerosols and trace gases
• Validation results imply that in the further development of
the surface UV algorithm we need to focus on
• Correction for absorbing aerosols and trace gases
• Surface albedo climatology
• The validation tools developed, and the ground-based
data gathered for this study lay a good basis for further
development of the OMI surface UV algorithm
• User feedback has been of great help and gives us
motivation for further work.